On the impact of the galaxy window function on cosmological parameter estimation

Abstract

One important source of systematics in galaxy redshift surveys comes from the estimation of the galaxy window function. Up until now, the impact of the uncertainty in estimating the galaxy window function on parameter inference has not been properly studied. In this paper, we show that the uncertainty and the bias in estimating the galaxy window function will be salient for ongoing and next-generation galaxy surveys using a simulation-based approach. With a specific case study of cross-correlating Emission-line galaxies from the DESI Legacy Imaging Surveys and the Planck CMB lensing map, we show that neural network-based regression approaches to modelling the window function are superior in comparison to linear regression-based models. We additionally show that the definition of the galaxy overdensity estimator can impact the overall signal-to-noise of observed power spectra. Finally, we show that the additive biases coming from the window functions can significantly bias the modes of the inferred parameters and also degrade their precision. Thus, a careful understanding of the window functions will be essential to conduct cosmological experiments.

arXiv comment13 pages, 12 figures, complementary paper to an upcoming paper on Cross-Correlation of ELGs and Planck CMB lensing, accepted for publication in MNRAS

References in corpus (22)

• Array Programming with NumPy
• Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds
• emcee: The MCMC Hammer
• Gaia Data Release 2. Summary of the contents and survey properties
• HEALPix -- a Framework for High Resolution Discretization, and Fast Analysis of Data Distributed on the Sphere
• Efficient Computation of CMB anisotropies in closed FRW models
• Overview of the DESI Legacy Imaging Surveys
• The DESI Experiment Part I: Science,Targeting, and Survey Design
• Planck 2018 results. VIII. Gravitational lensing
• MASTER of the CMB Anisotropy Power Spectrum: A Fast Method for Statistical Analysis of Large and Complex CMB Data Sets
• The Atacama Cosmology Telescope: DR4 Maps and Cosmological Parameters
• Cosmology Intertwined III: $f{\sigma }_8$ and $S_8$
• Core Cosmology Library: Precision Cosmological Predictions for LSST
• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Analysis of potential systematics
• Arbitrating the $S_8$ discrepancy with growth rate measurements from Redshift-Space Distortions
• Dark Energy Survey Year 1 Results: Galaxy clustering for combined probes
• Excess Clustering on Large Scales in the MegaZ DR7 Photometric Redshift Survey
• Calibration errors unleashed: effects on cosmological parameters and requirements for large-scale structure surveys
• Improving Galaxy Clustering Measurements with Deep Learning: analysis of the DECaLS DR7 data
• Mitigating contamination in LSS surveys: a comparison of methods
• Primordial non-Gaussianity from the Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey I: Catalogue Preparation and Systematic Mitigation
• Accurate estimation of angular power spectra for maps with correlated masks

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• Constraining primordial non-Gaussianity from DESI quasar targets and Planck CMB lensing
• Measuring ${\sigma }_8$ using DESI Legacy Imaging Surveys Emission-Line Galaxies and Planck CMB Lensing and the Impact of Dust on Parameter Inference
• Pair Counting without Binning -- A New Approach to Correlation Functions in Clustering Statistics